Giving it up is rational thinking, because there is no "it" there when the label is too broad.

In Bayesian inference, it is equivalent to P( A | B v C v D v ...), which is somewhat like underfitting. The space of possibilities becomes too large for it to be possible to find a good move. In games it is precisely the unclear parts of the game space that is interesting to the loosing part, because it is most likely there will be better moves there. But when it is not even possible to analyze those parts, then true optimal play regresses to quarreling about it, which is precisely what the Japanese tradition has done for at least some hundred years.

I have played enough Go to know that the concrete rules can make the endgame very different. The usual practice is to pretend it is not so, and stop the game before the endgame starts.

So Go is riddled with quarrels and pretense. Not a game in practice. More like politics, or Zen.

Optimal playing strategies in games can be very different from what people believe them to be, as examplified by the program Eurisko which won the Traveller TCS championships with very unconventional fleets. I suspect strongly that similar thing will happen for true Go games.

I might have found a variation of minimax that can tackle Go, but to use it, it MUST be possible to evaluate a Go position, at least in principle. So I will probably go for the Tromp-Taylor rules, if I get the time to do this. And perhaps the Japanese rules of Robert Jasiek.